In the field of personal identification and data retrieval, data collating devices, which use input data as a collate key, and collate the data with registered data within a database to output the result of collation, have been widely used. However, in the conventional data collating devices, as the registered data increase, the amount of memory required for the database increases, resulting in a problem of high costs and the necessity of having to provide a firm system construction. Thus, there have been urgent demands for means and methods for effectively solving the above-mentioned conventional problems.
FIG. 15 is a block diagram that shows a construction of a conventional data collating device. The input section 10 is used for inputting physical amount data such as audio data and image data. The feature amount extracting section 20 extracts feature amount data from physical amount data input through the input section 10. This feature amount data is/are data for characterizing the physical amount data.
More specifically, when physical data is audio data, voice pitches, power, spectrum, etc., are used as the feature amount data. On the other hand, when the physical data is image data, density histograms, edges, etc., are used as the feature amount data. The switch 30 switches the output destinations of the feature amount data from the feature amount extracting section 20.
When the feature amount data is registered in a database 50, the switch 30 switches the output destinations of the feature amount data from the feature amount extracting section 20 to the writing section 40. When the feature amount data is collated with any feature amount data within the database 50, the switch 30 switches the output destinations of the feature amount data from the feature amount extracting section 20 to a collating section 60.
Upon registering the feature amount data, the writing section 40 writes the feature amount data that is successively output from the feature amount extracting section 20 in the database 50. Therefore, a plurality of pieces of the feature amount data is stored in the database 50. Upon collation, the collating section 60 collates the feature amount data (collation key) output from the feature amount extracting section 20 with a plurality of pieces of the feature amount data stored in the database 50, and outputs the result of collation (coincidence or non-coincidence).
Operations of the data collating device will be explained below. However, for the sake of simplicity, operations upon registration and collation will be explained separately. Upon registration, the switch 30 is switched by a control unit (not shown) towards the side of the writing section 40. In this state, when physical amount data to be registered is input from the input section 10, the feature amount extracting section 20 extracts feature amount data from the physical amount data. This feature amount data is input to the writing section 40 through the switch 30. Thus, the writing section 40 writes the feature amount data in the database 50. Thereafter, the above-mentioned operations are repeated so that a plurality of pieces of the feature amount data is successively stored in the database 50.
Upon collation, the switch 30 is switched by the control unit (not shown) towards the side of the collating section 60. In this state, when physical amount data is input from the input section 10 as a collation key, the feature amount extracting section 20 extracts feature amount data from the physical amount data as collation key. This feature amount data is input to the collating section 60 through the switch 30. Thus, the collating section 60 successively collates this feature amount data with a plurality of pieces of the feature amount data stored in the database 50 using the input feature amount data as a key, and then outputs the result of collation (coincidence or non-coincidence).
Thus, all the pieces of the feature amount data extracted by the feature amount extracting section 20 upon registration are stored in the database 50, and upon collation, these pieces of the feature amount data serving as collation keys are collated with feature amount data within the database 50.
However, since all the pieces of the feature amount data are stored in the database 50, there arises a drawback that more amount of memory needs to be prepared as the feature amount data to be registered increases. This problem becomes more serious when a large amount of data is handled, because, great amount of memory is required whereby the cost increases.